Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

Abstract

Wire + arc additive manufacturing (WAAM) utilizes a welding arc as a heat source and a metal wire as a feedstock. In recent years, WAAM has attracted significant attention in the manufacturing industry owing to its advantages: (1) high deposition rate, (2) low system setup cost, (3) wide diversity of wire materials, and (4) sustainability for constructing large-sized metal structures. However, owing to the complexity of arc welding in WAAM, more research efforts are required to improve its process repeatability and advance part qualification. This study proposes a methodology to detect defects of the arch welding process in WAAM using images acquired by a high dynamic range camera. The gathered images are preprocessed to emphasize features and used for an artificial intelligence model to classify normal and abnormal statuses of arc welding in WAAM. Owing to the shortage of image datasets for defects, transfer learning technology is adopted. In addition, to understand and check the basis of the model’s feature learning, a gradient-weighted class activation mapping algorithm is applied to select a model that has the correct judgment criteria. Experimental results show that the detection accuracy of the metal transfer region-of-interest (RoI) reached 99%, whereas that of the weld-pool and bead RoI was 96%.

Keywords

• wire + arc additive manufacturing (WAAM)
• convolutional neural networks
• transfer learning
• artificial intelligence
• defect detection